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World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

Tomb rAIder: AI Search with Kotlin

Dmytro Kurets

Pure vector search fails at hard constraints like strict price limits. Master a hybrid AI search pipeline in Kotlin to deliver highly accurate query results.

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#1 about 1 min

Introduction to practical AI search constraints

Local embeddings and Kotlin enable building computationally grounded and realistic constrained searches.

#2 about 2 min

Selecting tools for a local AI search system

Spring Boot, pgvector, and local LLMs provide a robust foundation for building data retrieval applications.

#3 about 3 min

Calculating and storing embeddings in PostgreSQL

Textual metadata converts into high-dimensional vectors stored in a database for fast proximity scanning.

#4 about 7 min

Evaluating the limitations of pure semantic search

Single-vector semantic matching fails to reliably honor strict numeric and boolean boundaries.

#5 about 3 min

Applying hard constraints with SQL filters

Combining relational filters for exact bounds alongside vector distance queries limits errors and improves overall relevance.

#6 about 4 min

Decomposing search intents with LLM prompts

Generative models can decompose complex raw queries into explicit filter parameters and vague text intents.

#7 about 7 min

Replacing LLMs with specialized data extractors

Domain-specific parsing utilities extract values like precise pricing and amenity inclusions more efficiently than large general language models.

#8 about 6 min

Running asynchronous query extraction and intent processing

Concurrent execution of independent domain extractors securely isolates the remaining unstructured fuzzy intentions without creating performance bottlenecks.

#9 about 3 min

Implementing custom search logic with Spring AI

Spring AI libraries seamlessly compile embedding API requests dynamically into backend relational database retrieval commands.

#10 about 4 min

Demonstrating the completed multi-step search engine

A layered search architecture balances explicitly strict requirements with fuzzy phrase semantics to provide highly accurate user responses.

#11 about 2 min

Core principles for robust AI search systems

Applying traditional relational datasets filters prior to probabilistic comparisons is essential for high-fidelity scalable operations.

Matching moments

2:05 min

Designing highly scalable hybrid AI search engines

Jan Schweiger · WWC 2022

3:37 min

Demonstrating semantic latency reductions using Spring AI configurations

1:03 min

Revolutionizing database search queries with language models

Calvin Seward Calvin Seward · Europe 2026 Virtual

3:46 min

Substituting traditional classification models with search-based AI architecture

Erik Bamberg · LIVE

2:35 min

Postgres migration performance results and future semantic search

Dharin Shah Dharin Shah · WWC 2025

1:36 min

Differentiating AI search layers from standard LLMs

Klaus-M. Schremser Klaus-M. Schremser · WWC Europe 2026

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